def structure_discrete_hmsvm_bmrm(m_data_dict=data_dict):
from modshogun import RealMatrixFeatures, SequenceLabels, HMSVMModel, Sequence, TwoStateModel
from modshogun import StructuredAccuracy, SMT_TWO_STATE
try:
from modshogun import DualLibQPBMSOSVM
except ImportError:
print("DualLibQPBMSOSVM not available")
exit(0)
labels_array = m_data_dict['label'][0]
idxs = numpy.nonzero(labels_array == -1)
labels_array[idxs] = 0
labels = SequenceLabels(labels_array, 250, 500, 2)
features = RealMatrixFeatures(m_data_dict['signal'].astype(float), 250,
500)
num_obs = 4 # given by the data file used
model = HMSVMModel(features, labels, SMT_TWO_STATE, num_obs)
sosvm = DualLibQPBMSOSVM(model, labels, 5000.0)
sosvm.train()
#print sosvm.get_w()
predicted = sosvm.apply(features)
evaluator = StructuredAccuracy()
acc = evaluator.evaluate(predicted, labels)
def structure_discrete_hmsvm_bmrm (m_data_dict=data_dict):
from modshogun import RealMatrixFeatures, SequenceLabels, HMSVMModel, Sequence, TwoStateModel
from modshogun import StructuredAccuracy, SMT_TWO_STATE
try:
from modshogun import DualLibQPBMSOSVM
except ImportError:
print("DualLibQPBMSOSVM not available")
exit(0)
labels_array = m_data_dict['label'][0]
idxs = numpy.nonzero(labels_array == -1)
labels_array[idxs] = 0
labels = SequenceLabels(labels_array, 250, 500, 2)
features = RealMatrixFeatures(m_data_dict['signal'].astype(float), 250, 500)
num_obs = 4 # given by the data file used
model = HMSVMModel(features, labels, SMT_TWO_STATE, num_obs)
sosvm = DualLibQPBMSOSVM(model, labels, 5000.0)
sosvm.train()
#print sosvm.get_w()
predicted = sosvm.apply(features)
evaluator = StructuredAccuracy()
acc = evaluator.evaluate(predicted, labels)
def structure_plif_hmsvm_bmrm (num_examples, example_length, num_features, num_noise_features): from modshogun import RealMatrixFeatures, TwoStateModel, DualLibQPBMSOSVM, StructuredAccuracy model = TwoStateModel.simulate_data(num_examples, example_length, num_features, num_noise_features) sosvm = DualLibQPBMSOSVM(model, model.get_labels(), 5000.0) sosvm.train() #print sosvm.get_w() predicted = sosvm.apply(model.get_features()) evaluator = StructuredAccuracy() acc = evaluator.evaluate(predicted, model.get_labels())
def structure_plif_hmsvm_mosek (num_examples, example_length, num_features, num_noise_features):
from modshogun import RealMatrixFeatures, TwoStateModel, StructuredAccuracy
try:
from modshogun import PrimalMosekSOSVM
except ImportError:
print("Mosek not available")
return
model = TwoStateModel.simulate_data(num_examples, example_length, num_features, num_noise_features)
sosvm = PrimalMosekSOSVM(model, model.get_labels())
sosvm.train()
#print(sosvm.get_w())
predicted = sosvm.apply(model.get_features())
evaluator = StructuredAccuracy()
acc = evaluator.evaluate(predicted, model.get_labels())
def structure_plif_hmsvm_bmrm (num_examples, example_length, num_features, num_noise_features):
from modshogun import RealMatrixFeatures, TwoStateModel, StructuredAccuracy
try:
from modshogun import DualLibQPBMSOSVM
except ImportError:
print("DualLibQPBMSOSVM not available")
exit(0)
model = TwoStateModel.simulate_data(num_examples, example_length, num_features, num_noise_features)
sosvm = DualLibQPBMSOSVM(model, model.get_labels(), 5000.0)
sosvm.set_store_train_info(False)
sosvm.train()
#print sosvm.get_w()
predicted = sosvm.apply(model.get_features())
evaluator = StructuredAccuracy()
acc = evaluator.evaluate(predicted, model.get_labels())
def structure_plif_hmsvm_mosek(num_examples, example_length, num_features,
num_noise_features):
from modshogun import RealMatrixFeatures, TwoStateModel, StructuredAccuracy
try:
from modshogun import PrimalMosekSOSVM
except ImportError:
print("Mosek not available")
return
model = TwoStateModel.simulate_data(num_examples, example_length,
num_features, num_noise_features)
sosvm = PrimalMosekSOSVM(model, model.get_labels())
sosvm.train()
#print(sosvm.get_w())
predicted = sosvm.apply(model.get_features())
evaluator = StructuredAccuracy()
acc = evaluator.evaluate(predicted, model.get_labels())
def structure_hierarchical_multilabel_classification(train_file_name,
test_file_name):
train_file = open(train_file_name)
test_file = open(test_file_name)
train_features, train_labels, train_taxonomy = get_features_labels(
train_file)
model = HierarchicalMultilabelModel(train_features, train_labels,
train_taxonomy)
sgd = StochasticSOSVM(model, train_labels)
t1 = time.time()
sgd.train()
print('>>> Took %f time for training' % (time.time() - t1))
test_features, test_labels, test_taxonomy = get_features_labels(test_file)
assert(test_taxonomy.all() == train_taxonomy.all())
evaluator = StructuredAccuracy()
outlabel = LabelsFactory.to_structured(sgd.apply(test_features))
print('>>> Accuracy of classification = %f' % evaluator.evaluate(
outlabel, test_labels))
### prepare training, test data and evaluator train_data_file = 'hmsvm_%d_distort_data_fold' % distort train_num_examples_fold = 20 train_num_folds = 5 train_labels, train_features = utils.unfold_data(train_data_file) test_data_file = 'hmsvm_%d_distort_data_test' % distort test_num_examples = 100 test_labels, test_features = utils.read_mat_file(test_data_file, test_num_examples) ### train ML-HMM and evaluate in training data model = HMSVMModel(train_features, train_labels, SMT_TWO_STATE) model.set_use_plifs(True) mlhmm = MLHMM(model) mlhmm.train() prediction = mlhmm.apply() accuracy = evaluator.evaluate(prediction, train_labels) print '\ttraining accuracy:\t' + str(accuracy*100) + '%' utils.print_statistics(train_labels, prediction) ### evaluate in test data prediction = mlhmm.apply(test_features) accuracy = evaluator.evaluate(prediction, test_labels) print '\ttest accuracy:\t\t' + str(accuracy*100) + '%' utils.print_statistics(test_labels, prediction)
wdists = np.array(res.get_hist_wdist_vector())
plt.figure()
plt.subplot(221)
plt.title("Fp and Fd history")
plt.plot(xrange(res.get_n_iters()), Fps, hold=True)
plt.plot(xrange(res.get_n_iters()), Fds, hold=True)
plt.subplot(222)
plt.title("w dist history")
plt.plot(xrange(res.get_n_iters()), wdists)
# Evaluation
out = sosvm.apply()
Evaluation = StructuredAccuracy()
acc = Evaluation.evaluate(out, labels)
print "Correct classification rate: %0.4f%%" % (100.0 * acc)
# show figure
Z = get_so_labels(sosvm.apply(RealFeatures(X2)))
x = (X2[0, :]).reshape(cnt, cnt)
y = (X2[1, :]).reshape(cnt, cnt)
z = Z.reshape(cnt, cnt)
plt.subplot(223)
plt.pcolor(x, y, z, shading="interp")
plt.contour(x, y, z, linewidths=1, colors="black", hold=True)
plt.plot(X[:, 0], X[:, 1], "yo")
plt.axis("tight")
plt.title("Classification")
wdists = np.array(res.get_hist_wdist_vector())
plt.figure()
plt.subplot(221)
plt.title('Fp and Fd history')
plt.plot(xrange(res.get_n_iters()), Fps, hold=True)
plt.plot(xrange(res.get_n_iters()), Fds, hold=True)
plt.subplot(222)
plt.title('w dist history')
plt.plot(xrange(res.get_n_iters()), wdists)
# Evaluation
out = sosvm.apply()
Evaluation = StructuredAccuracy()
acc = Evaluation.evaluate(out, labels)
print "Correct classification rate: %0.4f%%" % (100.0 * acc)
# show figure
Z = get_so_labels(sosvm.apply(RealFeatures(X2)))
x = (X2[0, :]).reshape(cnt, cnt)
y = (X2[1, :]).reshape(cnt, cnt)
z = Z.reshape(cnt, cnt)
plt.subplot(223)
plt.pcolor(x, y, z, shading='interp')
plt.contour(x, y, z, linewidths=1, colors='black', hold=True)
plt.plot(X[:, 0], X[:, 1], 'yo')
plt.axis('tight')
plt.title('Classification')
evaluator = StructuredAccuracy() ### train ML-HMM and evaluate in training data print 'training ML-HMM' model = HMSVMModel(train_features, train_labels, SMT_TWO_STATE) model.set_use_plifs(True) mlhmm = MLHMM(model) mlhmm.train() ''' print '\n\tmodel parameters:' print '\t- transition scores: ' + str(numpy.exp(mlhmm.transition_scores)) print '\t- feature scores:' for s,f in product(xrange(mlhmm.num_free_states), xrange(mlhmm.num_features)): print '\t\tstate %d feature %d:\n%s' % (s, f, str(numpy.exp(mlhmm.feature_scores[f,s,:]))) ''' prediction = mlhmm.apply() accuracy = evaluator.evaluate(prediction, train_labels) print '\n\ttraining accuracy: ' + str(accuracy * 100) + '%' utils.print_statistics(train_labels, prediction) ### evaluate in test data print 'testing ML-HMM' prediction = mlhmm.apply(test_features) accuracy = evaluator.evaluate(prediction, test_labels) print '\ttest accuracy: ' + str(accuracy * 100) + '%' utils.print_statistics(test_labels, prediction)